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Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal.  These measurements are usually much smaller than the number of samples that define the signal.  From these small numbers of measurements, the signal is then reconstructed by non-linear procedure.  Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways.  In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.

Imaging. --- Signal processing -- Mathematics. --- Signal processing. --- Vision. --- Electrical & Computer Engineering --- Engineering & Applied Sciences --- Telecommunications --- Applied Physics --- Electrical Engineering --- Signal processing --- Computer vision --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Digital techniques --- Mathematics. --- Eyesight --- Seeing --- Sight --- Engineering. --- Image processing. --- Signal, Image and Speech Processing. --- Image Processing and Computer Vision. --- Senses and sensation --- Blindfolds --- Eye --- Physiological optics --- Computer vision. --- Speech processing systems. --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Optical equipment --- Digital techniques&delete& --- Mathematics

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The book presents novel research in the areas of social identity and security when using mobile platforms. The topics cover a broad range of applications related to securing social identity as well as the latest advances in the field, including the presentation of novel research methods that are in the service of all citizens using mobile devices. More specifically, academic, industry-related and government (law enforcement, intelligence and defence) organizations, will benefit from the research topics of this book that cover the concept of identity management and security using mobile platforms from various perspectives, i.e. whether a user navigates to social media, accesses their own phone devices, access their bank accounts, uses online shopping service providers, accesses their personal documents or accounts with valuable information, surfs the internet, or even becomes a victim of cyberattacks. In all of the aforementioned cases, there is a need for mobile related technologies that protect the users’ social identity and well-being in the digital world, including the use of biometrics, cybersecurity software and tools, active authentication and identity anti-spoofing algorithms and more.

Computer security. --- Computer privacy --- Computer system security --- Computer systems --- Computers --- Cyber security --- Cybersecurity --- Electronic digital computers --- Protection of computer systems --- Security of computer systems --- Data protection --- Security systems --- Hacking --- Protection --- Security measures --- Computers and civilization. --- System safety. --- Social sciences—Data processing. --- Social sciences—Computer programs. --- Computer crimes. --- Biometrics (Biology). --- Computers and Society. --- Security Science and Technology. --- Computational Social Sciences. --- Cybercrime. --- Biometrics. --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Computers and crime --- Cyber crimes --- Cybercrimes --- Electronic crimes (Computer crimes) --- Internet crimes --- Crime --- Privacy, Right of --- Safety, System --- Safety of systems --- Systems safety --- Accidents --- Industrial safety --- Systems engineering --- Civilization and computers --- Civilization --- Statistical methods --- Prevention

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This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field. Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra. This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. Nalini K. Ratha is Empire Innovation professor in the Department of Computer Science and Engineering at University at Buffalo (New York). He is co-author and co-editor, respectively, of the Springer books, Guide to Biometrics and Advances in Biometrics. Vishal M. Patel is Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University (JHU). Rama Chellappa is Bloomberg Distinguished Professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering at JHU. He is co-author and co-editor, respectively, of the Springer books, Unconstrained Face Recognition and Handbook of Remote Biometrics.

Optical data processing. --- Biometrics (Biology). --- Machine learning. --- Neural networks (Computer science) . --- Image Processing and Computer Vision. --- Biometrics. --- Machine Learning. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Learning, Machine --- Machine theory --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Statistical methods --- Optical equipment --- Human face recognition (Computer science) --- Face recognition, Human (Computer science) --- Facial pattern recognition (Computer science) --- Optical pattern recognition

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The book presents novel research in the areas of social identity and security when using mobile platforms. The topics cover a broad range of applications related to securing social identity as well as the latest advances in the field, including the presentation of novel research methods that are in the service of all citizens using mobile devices. More specifically, academic, industry-related and government (law enforcement, intelligence and defence) organizations, will benefit from the research topics of this book that cover the concept of identity management and security using mobile platforms from various perspectives, i.e. whether a user navigates to social media, accesses their own phone devices, access their bank accounts, uses online shopping service providers, accesses their personal documents or accounts with valuable information, surfs the internet, or even becomes a victim of cyberattacks. In all of the aforementioned cases, there is a need for mobile related technologies that protect the users’ social identity and well-being in the digital world, including the use of biometrics, cybersecurity software and tools, active authentication and identity anti-spoofing algorithms and more.

Criminal law. Criminal procedure --- Social sciences --- Biomathematics. Biometry. Biostatistics --- Applied physical engineering --- Computer architecture. Operating systems --- Information systems --- Computer. Automation --- biomathematica --- computers --- sociale wetenschappen --- computerprogramma's --- ingenieurswetenschappen --- computercriminaliteit --- gegevensverwerking

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Electricity --- Telecommunication technology --- Electrical engineering --- Applied physical engineering --- Mass communications --- Computer. Automation --- Equipment, services, installations in buildings --- beeldverwerking --- elektrische netwerken --- spraaktechnologie --- tekstverwerking --- communicatietechnologie --- elektrische machines --- signaalverwerking --- elektriciteitsproductie --- elektriciteitsdistributie